OpenRaiser/Intern-Atlas huggingface dataset:
Dual Interior Point Optimization Learning
Venue: arXiv cs.LG 2025
Year: 2025

In many practical applications of constrained optimization, scale and solving time limits make traditional optimization solvers prohibitively slow. Thus, the research question of how to design optimization proxies -- machine learning models that produce high-quality solutions -- has recently received significant attention. Orthogonal to this research thread which focuses on learning primal solutions, this paper studies how to learn dual feasible solutions that complement primal approaches and provide quality guarantees. The paper makes two distinct contributions. First, to train dual linear optimization proxies, the paper proposes a smoothed self-supervised loss function that augments the objective function with a dual penalty term. Second, the paper proposes a novel dual completion strategy that guarantees dual feasibility by solving a convex optimization problem. Moreover, the paper derives closed-form solutions to this completion optimization for several classes of dual penalties, eliminating the need for computationally-heavy implicit layers. Numerical results are presented on large linear optimization problems and demonstrate the effectiveness of the proposed approach. The proposed dual completion outperforms methods for learning optimization proxies which do not exploit the structure of the dual problem. Compared to commercial optimization solvers, the learned dual proxies achieve optimality gaps below $1\%$ and several orders of magnitude speedups.
A Complete Survey on Contemporary Methods, Emerging Paradigms and Hybrid Approaches for Few-Shot Learning
Venue: arXiv cs.LG 2025
Year: 2025

Despite the widespread success of deep learning, its intense requirements for vast amounts of data and extensive training make it impractical for various real-world applications where data is scarce. In recent years, Few-Shot Learning (FSL) has emerged as a learning paradigm that aims to address these limitations by leveraging prior knowledge to enable rapid adaptation to novel learning tasks. Due to its properties that highly complement deep learning's data-intensive needs, FSL has seen significant growth in the past few years. This survey provides a comprehensive overview of both well-established methods as well as recent advancements in the FSL field. The presented taxonomy extends previously proposed ones by incorporating emerging FSL paradigms, such as in-context learning, along with novel categories within the meta-learning paradigm for FSL, including neural processes and probabilistic meta-learning. Furthermore, a holistic overview of FSL is provided by discussing hybrid FSL approaches that extend FSL beyond the typically examined supervised learning setting. The survey also explores FSL's diverse applications across various domains. Finally, recent trends shaping the field, outstanding challenges, and promising future research directions are discussed.
The Social Sphere Model: Heuristic Influence Prediction in Evolving Networks
Venue: arXiv cs.SI 2025
Year: 2025

How would admissions look like in a university program for influencers? In the realm of social network analysis, influence maximization and link prediction stand out as pivotal challenges. Influence maximization focuses on identifying a set of key nodes to maximize information dissemination, while link prediction aims to foresee potential connections within the network. These strategies, primarily deep learning link prediction methods and greedy algorithms, have been previously used in tandem to identify future influencers. However, given the complexity of these tasks, especially in large-scale networks, we propose an algorithm, The Social Sphere Model, which uniquely utilizes expected value in its future graph prediction and combines specifically path-based link prediction metrics and heuristic influence maximization strategies to effectively identify future vital nodes in weighted networks. Our approach is tested on two distinct contagion models, offering a promising solution with lower computational demands. This advancement not only enhances our understanding of network dynamics but also opens new avenues for efficient network management and influence strategy development.
Information-Theoretic Active Correlation Clustering
Venue: arXiv cs.LG 2025
Year: 2025

Correlation clustering is a flexible framework for partitioning data based solely on pairwise similarity or dissimilarity information, without requiring the number of clusters as input. However, in many practical scenarios, these pairwise similarities are not available a priori and must be obtained through costly measurements or human feedback. This motivates the use of active learning to query only the most informative pairwise comparisons, enabling effective clustering under budget constraints. In this work, we develop a principled active learning approach for correlation clustering by introducing several information-theoretic acquisition functions that prioritize queries based on entropy and expected information gain. These strategies aim to reduce uncertainty about the clustering structure as efficiently as possible. We evaluate our methods across a range of synthetic and real-world settings and show that they significantly outperform existing baselines in terms of clustering accuracy and query efficiency. Our results highlight the benefits of combining active learning with correlation clustering in settings where similarity information is costly or limited.
Multimodal Rationales for Explainable Visual Question Answering
Venue: arXiv cs.CV 2025
Year: 2025

Visual Question Answering (VQA) is a challenging task of predicting the answer to a question about the content of an image. Prior works directly evaluate the answering models by simply calculating the accuracy of predicted answers. However, the inner reasoning behind the predictions is disregarded in such a "black box" system, and we cannot ascertain the trustworthiness of the predictions. Even more concerning, in some cases, these models predict correct answers despite focusing on irrelevant visual regions or textual tokens. To develop an explainable and trustworthy answering system, we propose a novel model termed MRVQA (Multimodal Rationales for VQA), which provides visual and textual rationales to support its predicted answers. To measure the quality of generated rationales, a new metric vtS (visual-textual Similarity) score is introduced from both visual and textual perspectives. Considering the extra annotations distinct from standard VQA, MRVQA is trained and evaluated using samples synthesized from some existing datasets. Extensive experiments across three EVQA datasets demonstrate that MRVQA achieves new state-of-the-art results through additional rationale generation, enhancing the trustworthiness of the explainable VQA model. The code and the synthesized dataset are released under https://github.com/lik1996/MRVQA2025.
Measuring Machine Learning Harms from Stereotypes Requires Understanding Who Is Harmed by Which Errors in What Ways
Venue: arXiv cs.CY 2025
Year: 2025

As machine learning applications proliferate, we need an understanding of their potential for harm. However, current fairness metrics are rarely grounded in human psychological experiences of harm. Drawing on the social psychology of stereotypes, we use a case study of gender stereotypes in image search to examine how people react to machine learning errors. First, we use survey studies to show that not all machine learning errors reflect stereotypes nor are equally harmful. Then, in experimental studies we randomly expose participants to stereotype-reinforcing, -violating, and -neutral machine learning errors. We find stereotype-reinforcing errors induce more experientially (i.e., subjectively) harmful experiences, while having minimal changes to cognitive beliefs, attitudes, or behaviors. This experiential harm impacts women more than men. However, certain stereotype-violating errors are more experientially harmful for men, potentially due to perceived threats to masculinity. We conclude that harm cannot be the sole guide in fairness mitigation, and propose a nuanced perspective depending on who is experiencing what harm and why.
Capacity Modification in the Stable Matching Problem
Venue: arXiv cs.GT 2025
Year: 2025

We study the problem of capacity modification in the many-to-one stable matching of workers and firms. Our goal is to systematically study how the set of stable matchings changes when some seats are added to or removed from the firms. We make three main contributions: First, we examine whether firms and workers can improve or worsen upon changing the capacities under worker-proposing and firm-proposing deferred acceptance algorithms. Second, we study the computational problem of adding or removing seats to either match a fixed worker-firm pair in some stable matching or make a fixed matching stable with respect to the modified problem. We develop polynomial-time algorithms for these problems when only the overall change in the firms' capacities is restricted, and show NP-hardness when there are additional constraints for individual firms. Lastly, we compare capacity modification with the classical model of preference manipulation by firms and identify scenarios under which one mode of manipulation outperforms the other. We find that a threshold on a given firm's capacity, which we call its peak, crucially determines the effectiveness of different manipulation actions.
Ten simple rules for training scientists to make better software
Venue: arXiv cs.CY 2025
Year: 2025

Computational methods and associated software implementations are central to every field of scientific investigation. Modern biological research, particularly within systems biology, has relied heavily on the development of software tools to process and organize increasingly large datasets, simulate complex mechanistic models, provide tools for the analysis and management of data, and visualize and organize outputs. However, developing high-quality research software requires scientists to develop a host of software development skills, and teaching these skills to students is challenging. There has been a growing importance placed on ensuring reproducibility and good development practices in computational research. However, less attention has been devoted to informing the specific teaching strategies which are effective at nurturing in researchers the complex skillset required to produce high-quality software that, increasingly, is required to underpin both academic and industrial biomedical research. Recent articles in the Ten Simple Rules collection have discussed the teaching of foundational computer science and coding techniques to biology students. We advance this discussion by describing the specific steps for effectively teaching the necessary skills scientists need to develop sustainable software packages which are fit for (re-)use in academic research or more widely. Although our advice is likely to be applicable to all students and researchers hoping to improve their software development skills, our guidelines are directed towards an audience of students that have some programming literacy but little formal training in software development or engineering, typical of early doctoral students. These practices are also applicable outside of doctoral training environments, and we believe they should form a key part of postgraduate training schemes more generally in the life sciences.
Pseudo-labelling meets Label Smoothing for Noisy Partial Label Learning
Venue: arXiv cs.CV 2025
Year: 2025

We motivate weakly supervised learning as an effective learning paradigm for problems where curating perfectly annotated datasets is expensive and may require domain expertise such as fine-grained classification. We focus on Partial Label Learning (PLL), a weakly-supervised learning paradigm where each training instance is paired with a set of candidate labels (partial label), one of which is the true label. Noisy PLL (NPLL) relaxes this constraint by allowing some partial labels to not contain the true label, enhancing the practicality of the problem. Our work centres on NPLL and presents a framework that initially assigns pseudo-labels to images by exploiting the noisy partial labels through a weighted nearest neighbour algorithm. These pseudo-label and image pairs are then used to train a deep neural network classifier with label smoothing. The classifier's features and predictions are subsequently employed to refine and enhance the accuracy of pseudo-labels. We perform thorough experiments on seven datasets and compare against nine NPLL and PLL methods. We achieve state-of-the-art results in all studied settings from the prior literature, obtaining substantial gains in the simulated fine-grained benchmarks. Further, we show the promising generalisation capability of our framework in realistic, fine-grained, crowd-sourced datasets.
IRFuzzer: Specialized Fuzzing for LLVM Backend Code Generation
Venue: arXiv cs.SE 2025
Year: 2025

Modern compilers, such as LLVM, are complex pieces of software. Due to their complexity, manual testing is unlikely to suffice, yet formal verification is difficult to scale. End-to-end fuzzing can be used, but it has difficulties in achieving high coverage of some components of LLVM. In this paper, we implement IRFuzzer to investigate the effectiveness of specialized fuzzing of the LLVM compiler backend. We focus on two approaches to improve the fuzzer: guaranteed input validity using constrained mutations and improved feedback quality. The mutator in IRFuzzer is capable of generating a wide range of LLVM IR inputs, including structured control flow, vector types, and function definitions. The system instruments coding patterns in the compiler to monitor the execution status of instruction selection. The instrumentation not only provides a new coverage feedback called matcher table coverage, but also provides an architecture specific guidance to the mutator. We show that IRFuzzer is more effective than existing fuzzers by fuzzing on 29 mature LLVM backend targets. In the process, we reported 74 confirmed new bugs in LLVM upstream, out of which 49 have been fixed, five have been back ported to LLVM 15, showing that specialized fuzzing provides useful and actionable insights to LLVM developers.
Assessment of the Sparsity-Diversity Trade-offs in Active Users Detection for mMTC with the Orthogonal Matching Pursuit
Venue: arXiv cs.IT 2025
Year: 2025

Wireless communication systems must increasingly support a multitude of machine-type communications (MTC) devices, thus calling for advanced strategies for active user detection (AUD). Recent literature has delved into AUD techniques based on compressed sensing, highlighting the critical role of signal sparsity. This study investigates the relationship between frequency diversity and signal sparsity in the AUD problem. Single-antenna users transmit multiple copies of non-orthogonal pilots across multiple frequency channels and the base station independently performs AUD in each channel using the orthogonal matching pursuit algorithm. We note that, although frequency diversity may improve the likelihood of successful reception of the signals, it may also damage the channel sparsity level, leading to important trade-offs. We show that a sparser signal significantly benefits AUD, surpassing the advantages brought by frequency diversity in scenarios with limited temporal resources and/or high numbers of receive antennas. Conversely, with longer pilots and fewer receive antennas, investing in frequency diversity becomes more impactful, resulting in a tenfold AUD performance improvement.
Localized and Distributed Beyond Diagonal Reconfigurable Intelligent Surfaces with Lossy Interconnections: Modeling and Optimization
Venue: arXiv cs.IT 2025
Year: 2025

Reconfigurable intelligent surface (RIS) is a key technology to control the communication environment in future wireless networks. Recently, beyond diagonal RIS (BD-RIS) emerged as a generalization of RIS achieving larger coverage through additional tunable impedance components interconnecting the RIS elements. However, conventional RIS and BD-RIS can effectively serve only users in their proximity, resulting in limited coverage. To overcome this limitation, in this paper, we investigate distributed RIS, whose elements are distributed over a wide region, in opposition to localized RIS commonly considered in the literature. The scaling laws of distributed BD-RIS reveal that it offers significant gains over distributed conventional RIS and localized BD-RIS, enabled by its interconnections allowing signal propagation within the BD-RIS. To assess the practical performance of distributed BD-RIS, we model and optimize BD-RIS with lossy interconnections through transmission line theory. Our model accounts for phase changes and losses over the BD-RIS interconnections arising when the interconnection lengths are not much smaller than the wavelength. Numerical results show that the performance of localized BD-RIS is only slightly impacted by losses, given the short interconnection lengths. Besides, distributed BD-RIS can achieve orders of magnitude of gains over conventional RIS, even in the presence of low losses.
EUGENE: Explainable Structure-aware Graph Edit Distance Estimation with Generalized Edit Costs
Venue: arXiv cs.LG 2025
Year: 2025

The need to identify graphs with small structural distances from a query arises in domains such as biology, chemistry, recommender systems, and social network analysis. Among several methods for measuring inter-graph distance, Graph Edit Distance (GED) is preferred for its comprehensibility, though its computation is hindered by NP-hardness. Optimization based heuristic methods often face challenges in providing accurate approximations. State-of-the-art GED approximations predominantly utilize neural methods, which, however: (i) lack an explanatory edit path corresponding to the approximated GED; (ii) require the NP-hard generation of ground-truth GEDs for training; and (iii) necessitate separate training on each dataset. In this paper, we propose EUGENE, an efficient, algebraic, and structure-aware optimization based method that estimates GED and also provides edit paths corresponding to the estimated cost. Extensive experimental evaluation demonstrates that EUGENE achieves state-of-the-art GED estimation with superior scalability across diverse datasets and generalized cost settings.
SPHINX-X: Scaling Data and Parameters for a Family of Multi-modal Large Language Models
Venue: arXiv cs.CV 2025
Year: 2025

We propose SPHINX-X, an extensive Multimodality Large Language Model (MLLM) series developed upon SPHINX. To improve the architecture and training efficiency, we modify the SPHINX framework by removing redundant visual encoders, bypassing fully-padded sub-images with skip tokens, and simplifying multi-stage training into a one-stage all-in-one paradigm. To fully unleash the potential of MLLMs, we assemble a comprehensive multi-domain and multimodal dataset covering publicly available resources in language, vision, and vision-language tasks. We further enrich this collection with our curated OCR intensive and Set-of-Mark datasets, extending the diversity and generality. By training over different base LLMs including TinyLlama1.1B, InternLM2-7B, LLaMA2-13B, and Mixtral8x7B, we obtain a spectrum of MLLMs that vary in parameter size and multilingual capabilities. Comprehensive benchmarking reveals a strong correlation between the multi-modal performance with the data and parameter scales. Code and models are released at https://github.com/Alpha-VLLM/LLaMA2-Accessory
One-Shot Coding over General Noisy Networks
Venue: arXiv cs.IT 2025
Year: 2025

We present a unified one-shot coding framework designed for the communication and compression of messages among multiple nodes across a general acyclic noisy network. Our setting can be seen as a one-shot version of the acyclic discrete memoryless network studied by Lee and Chung, and noisy network coding studied by Lim, Kim, El Gamal and Chung. We design a proof technique, called the exponential process refinement lemma, that is rooted in the Poisson matching lemma by Li and Anantharam, and can significantly simplify the analyses of one-shot coding over multi-hop networks. Our one-shot coding theorem not only recovers a wide range of existing asymptotic results, but also yields novel one-shot achievability results in different multi-hop network information theory problems, such as compress-and-forward and partial-decode-and-forward bounds for a one-shot (primitive) relay channel, and a bound for one-shot cascade multiterminal source coding. In a broader context, our framework provides a unified one-shot bound applicable to any combination of source coding, channel coding and coding for computing problems.
Keyframer: Empowering Animation Design using Large Language Models
Venue: arXiv cs.HC 2025
Year: 2025

Creating 2D animations is a complex, iterative process requiring continuous adjustments to movement, timing, and coordination of multiple elements within a scene. To support designers of varying levels of experience with animation design and implementation, we developed Keyframer, a design tool that generates animation code in response to natural language prompts, enabling users to preview rendered animations inline and edit them directly through provided editors. Through a user study with 13 novices and experts in animation design and programming, we contribute 1) a categorization of semantic prompt types for describing motion and identification of a 'decomposed' prompting style where users continually adapt their goals in response to generated output; and 2) design insights on supporting iterative refinement of animations through the combination of direct editing and natural language interfaces.
Masked LoGoNet: Fast and Accurate 3D Image Analysis for Medical Domain
Venue: arXiv cs.CV 2025
Year: 2025

Standard modern machine-learning-based imaging methods have faced challenges in medical applications due to the high cost of dataset construction and, thereby, the limited labeled training data available. Additionally, upon deployment, these methods are usually used to process a large volume of data on a daily basis, imposing a high maintenance cost on medical facilities. In this paper, we introduce a new neural network architecture, termed LoGoNet, with a tailored self-supervised learning (SSL) method to mitigate such challenges. LoGoNet integrates a novel feature extractor within a U-shaped architecture, leveraging Large Kernel Attention (LKA) and a dual encoding strategy to capture both long-range and short-range feature dependencies adeptly. This is in contrast to existing methods that rely on increasing network capacity to enhance feature extraction. This combination of novel techniques in our model is especially beneficial in medical image segmentation, given the difficulty of learning intricate and often irregular body organ shapes, such as the spleen. Complementary, we propose a novel SSL method tailored for 3D images to compensate for the lack of large labeled datasets. The method combines masking and contrastive learning techniques within a multi-task learning framework and is compatible with both Vision Transformer (ViT) and CNN-based models. We demonstrate the efficacy of our methods in numerous tasks across two standard datasets (i.e., BTCV and MSD). Benchmark comparisons with eight state-of-the-art models highlight LoGoNet's superior performance in both inference time and accuracy.